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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
import torchvision
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
min_keypoints_per_image = 10
def _count_visible_keypoints(anno):
return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)
def _has_only_empty_bbox(anno):
return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)
def has_valid_annotation(anno):
# if it's empty, there is no annotation
if len(anno) == 0:
return False
# if all boxes have close to zero area, there is no annotation
if _has_only_empty_bbox(anno):
return False
# keypoints task have a slight different critera for considering
# if an annotation is valid
if "keypoints" not in anno[0]:
return True
# for keypoint detection tasks, only consider valid images those
# containing at least min_keypoints_per_image
if _count_visible_keypoints(anno) >= min_keypoints_per_image:
return True
return False
class COCODataset(torchvision.datasets.coco.CocoDetection):
def __init__(
self, ann_file, root, remove_images_without_annotations, transforms=None
):
super(COCODataset, self).__init__(root, ann_file)
# sort indices for reproducible results
self.ids = sorted(self.ids)
# filter images without detection annotations
if remove_images_without_annotations:
ids = []
for img_id in self.ids:
ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = self.coco.loadAnns(ann_ids)
if has_valid_annotation(anno):
ids.append(img_id)
self.ids = ids
self.json_category_id_to_contiguous_id = {
v: i + 1 for i, v in enumerate(self.coco.getCatIds())
}
self.contiguous_category_id_to_json_id = {
v: k for k, v in self.json_category_id_to_contiguous_id.items()
}
self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
self.transforms = transforms
def __getitem__(self, idx):
img, anno = super(COCODataset, self).__getitem__(idx)
# filter crowd annotations
# TODO might be better to add an extra field
anno = [obj for obj in anno if obj["iscrowd"] == 0]
boxes = [obj["bbox"] for obj in anno]
boxes = torch.as_tensor(boxes).reshape(-1, 4) # guard against no boxes
target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
classes = [obj["category_id"] for obj in anno]
classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
classes = torch.tensor(classes)
target.add_field("labels", classes)
masks = [obj["segmentation"] for obj in anno]
masks = SegmentationMask(masks, img.size, mode='poly')
target.add_field("masks", masks)
if anno and "keypoints" in anno[0]:
keypoints = [obj["keypoints"] for obj in anno]
keypoints = PersonKeypoints(keypoints, img.size)
target.add_field("keypoints", keypoints)
target = target.clip_to_image(remove_empty=True)
if self.transforms is not None:
img, target = self.transforms(img, target)
return img, target, idx
def get_img_info(self, index):
img_id = self.id_to_img_map[index]
img_data = self.coco.imgs[img_id]
return img_data
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